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  "title": "Your Work Is Evaporating",
  "subtitle": "AI gave us more hands. It did not give us better memory.",
  "abstract": "A SharePlane article defining work evaporation as the loss of recoverable context around AI-assisted work and presenting a governed continuity system that extracts decisions, binds evidence, records provenance, and makes work reusable beyond the originating session.",
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        "id": "source:your-work-is-evaporating:generative-ai-at-work",
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        "title": "Generative AI at Work",
        "locator": "https://arxiv.org/abs/2304.11771",
        "role": "Supports the bounded claim that generative AI can increase work output in a defined customer-support setting.",
        "description": "A field study of 5,172 customer-support agents found a 15 percent average increase in issues resolved per hour after access to an AI assistant, with heterogeneous effects across workers. Caveat: The study concerns one organizational setting and task family. It does not establish universal productivity gains, measure knowledge retention, or evaluate SharePlane. The preservation-scaling conclusion is Tony Malott's inference from increased output volume.",
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        "title": "CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark",
        "locator": "https://arxiv.org/abs/2409.11363",
        "role": "Supports the distinction between having source materials and being able to reproduce a result.",
        "description": "A benchmark of 270 computational-reproducibility tasks drawn from 90 papers tested whether agents could reproduce results from provided code and data; the strongest evaluated agent reached 21 percent accuracy on the hardest task. Caveat: CORE-Bench evaluates scientific-computing reproducibility, not chat retrieval, personal knowledge management, or SharePlane. Applying its lesson to recoverability of AI-assisted work is an explicit cross-domain analogy by Tony Malott.",
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        "title": "AI Agents That Matter",
        "locator": "https://arxiv.org/abs/2407.01502",
        "role": "Supports preserving evaluation conditions beyond final-answer accuracy, including cost, holdout design, user needs, and reproducibility.",
        "description": "The paper identifies shortcomings in agent benchmarks, including narrow accuracy focus, neglected cost, conflated user needs, inadequate holdout sets, overfitting, and inconsistent evaluation practices. Caveat: The paper addresses agent evaluation methodology. It does not prescribe SharePlane's artifact model, and the recommendation to retain rejected approaches and unresolved risks is Tony Malott's operational synthesis.",
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        "title": "Pipeline Provenance for Analysis, Evaluation, Trust or Reproducibility",
        "locator": "https://arxiv.org/abs/2404.14378",
        "role": "Supports capturing processing provenance needed to reconstruct and evaluate computational results.",
        "description": "The paper presents PRAETOR, a software suite for automated generation, modeling, and analysis of provenance information for Python pipelines, arguing that captured processing information supports reproducibility and trust. Caveat: The work is scoped to computational and data-processing pipelines. Extending the principle to AI-assisted intellectual work and SharePlane is Tony Malott's architectural application.",
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      "text": "Work evaporation occurs when useful intellectual work still exists but cannot be reliably found, understood, trusted, continued, or reused when needed.",
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      "text": "Stored information is not equivalent to recoverable knowledge.",
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      "text": "Recoverable knowledge is not automatically reusable work.",
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      "text": "A conversation transcript is working context, not a finished knowledge artifact.",
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      "text": "Generative AI can materially increase the rate at which people produce potentially valuable work in bounded settings.",
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      "text": "When AI-assisted production rises without stronger preservation systems, preservation becomes a scaling problem.",
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      "caveat": "The cited study measures productivity, not preservation. The scaling conclusion is Tony Malott's inference."
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      "text": "Possession of source materials does not guarantee reproducibility or operational recoverability of a result.",
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      "text": "Reliable evaluation of AI agents requires more than final-answer accuracy and should retain relevant conditions such as cost, holdout posture, user needs, and reproducibility.",
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      "text": "For consequential AI-assisted work, rejected approaches, evaluation criteria, and unresolved risks may be part of the reusable intellectual asset.",
      "posture": "evidence-supported-owner-synthesis",
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      "caveat": "The source supports richer evaluation discipline; retention of rejected approaches and unresolved risks is Tony Malott's operational synthesis."
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      "caveat": "The three-layer model is Tony Malott's architecture, not a taxonomy proposed by the cited paper."
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      "text": "Extraction of durable value should be part of execution rather than deferred documentation cleanup.",
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